Advancing urban tree monitoring with AI-powered digital twins
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth’s surface. Their techniques could improve weather and drought prediction.
The new approach “nudges” existing climate simulations closer to future reality.
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.